Data Mining Practical Machine Learning Tools and Techniques

Color Image Processing – Methods and Applications

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

https://www.mdpi.com/2227-9717/9/5/834/pdf

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

This paper proposes a novel reversible picture information concealing plan over encoded area. Information inserting is accomplished through an open key balance system, in which access to the mystery encryption key isn't required. At the decoder side, an effective two-class SVM classifier is intended to recognize scrambled and non encoded picture patches, enabling us to mutually translate the installed message and the first picture flag. Contrasted and the best in class strategies, the proposed approach gives higher inserting limit and can consummately remake the first picture and also the implanted message. Broad exploratory outcomes are given to approve the prevalent execution of our plan.

Secure Reversible Image Data Hiding Over Encrypted Domain via Key Modulation

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

https://link.springer.com/content/pdf/10.1007/s11633-020-1257-9.pdf

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

The emergence of the internet of things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in  machine learning and the emergence of deep learning field,  and its ability to extract high-level features with high accuracy, these systems have been included to work with real network traffic CSE-CIC-IDS2018 for a wide range of intrusions and normal behavior as an ideal method of testing and evaluation. In this paper, we test and evaluate our deep model (DNN) which has achieved a good detection accuracy of about 90%.

/pdf/performance-analysis-of-flow-based-attacks-detection-on-cse-1o4ow1eqne.pdf

Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning

This paper presents a design for a honeypot smartphone system. The smartphone honeypot system has to perform several complex functions, the basic important three functions are: design and construct the system database, malware detection, and system reactions. During the construction of the system database various information, about the behaviour of various well known malicious applications is captured and saved in a database files using the hardware performance counters. Three features are used for this purpose: number of instructions, number of branches, and number of cache misses. A data set with 1260 malicious program is used to collect these features. One-dimensional Euclidian distance and multi-dimensional Euclidian distance are used to classify the samples from the data set to identify the family they belong to. Although the classification results were as low for some families, the algorithm is fully classified other families with 100% accuracy. The results indicate that the performance counters are good tools for detecting malware.

Designing a smartphone honeypot system using performance counters

Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-ofplane rotation on them around all the X,Y and Z axes. Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region. Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN). The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees. To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems.

Pose Invariant Palm Vein Identification System using Convolutional Neural Network

Video watermarking is one of the most widespread techniques amongst the many watermarking techniques presently are used; this is because the extreme existences of copyright abuse and misappropriation occur for video content. In this paper, a new watermarking algorithm is proposed to embed logo in digital video for copyright protection. To make the watermarks more robust to attack, host frame and host embedding indices must be changeable. A new algorithm is proposed to determined host frames by plasma function, Host location indices in frames are also determined by another plasma function. Logo is divided using the mosaic principle, the size of mosaic blocks is determined initially according to the degree of protection, whenever the size of mosaic blocks is small, it leads to safe embedding, and vice versa. Digital watermarks are embedded easily without any degradation for video quality, In the other side, the watermarked is retrieved by applying the reverse of proposed embedding algorithm and extracted watermark is still recognizable. The experimental results confirm that watermark is robust against three types of attacks which are addition of Gaussian noise, JPEG compression, and rotation process.

/pdf/video-mosaic-watermarking-using-plasma-key-3jvs24bgoa.pdf

Video mosaic watermarking using plasma key

Voice over Internet Protocol (VoIP) calls are susceptible to interfere at many points by many attackers, thus encryption considered an important part in keeping VoIP.
In this paper, Encryption VoIP based on Generated Biometric Key for RC4 Algorithm is proposed to encrypt the voice data before transmitting it over the network. The system uses a stream algorithm based on RC4 encryption with the new method of biometrics based Key generation technique. This system has generated complex keys in offline phase which is formed depend on features extracted using Linear Discernment Analysis (LDA) from face images.
The experimental work shows that the proposed system offers secrecy to speech data with voice cipher is unintelligible and the recovered voice has perfect quality with MSR equal to zero and PSNR equal to infinity.

/pdf/encryption-voip-based-on-generated-biometric-key-for-rc4-ea4x8io7pv.pdf

Nidaa F. Hassan

Papers

Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning

Designing a smartphone honeypot system using performance counters

Pose Invariant Palm Vein Identification System using Convolutional Neural Network

Video mosaic watermarking using plasma key

Encryption VoIP based on Generated Biometric Key for RC4 Algorithm